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JEET, Vol. 13, No. 1, January 2018
A Novel Speed Estimation Method of Induction Motors Using Real-Time Adaptive Extended Kalman Filter
Zhonggang Yin
Area B - Electric Machinery and Power Electronics
Abstract To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi?Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.
Keyword Induction motor (IM),Speed estimation,Real-time adaptive extended Kalman filter (RAEKF),Fuzzy factor
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